Modeling complex relationships between data has been a goal of computer scientists since computers were invented. There are several fields in which data is collected and analyzed in order to understand the relationship between various categories and types of related data. By understanding the relationships between these various categories and types of data, an analyst can identify patterns and relationships that govern and/or predict changes to the data. However, such analysis typically requires a deep understanding of the field associated with the data. It also typically requires a substantial understanding of mathematics, statistics and computer science.
One area where this is particularly true is in finance. For example, in the field of finance, there is currently a trend away from defined benefit retirement plans. Consequently, there has been a shift toward individual responsibility for financial and retirement planning. This shift makes it critical for the average investor to have a better understanding of financial entities and the investable assets associated with them, as well as their attributes, their connections, and any causal relationships in the context of the overall financial system. Therefore, the average investor needs to be better informed regarding the effects of investment decisions based on underlying financial concepts or strategies. More specifically, the average investor needs a vehicle that can assist in learning how to make rational decisions in light of competing financial concepts. Such investors need to understand the validity and value of these underlying competing financial concepts in order to make wise investment choices. One way in which investors would be able to make better decisions is by using mathematical and statistical modeling to back test these concepts on real historical data and/or analyze the impact of these concepts using projected future data. Such financial concepts, and the associated statistical and mathematical techniques for back testing and projecting forward, are generally taught in college finance and economics courses and beyond. However a vast majority of the general population does not make it college, and hence are deprived of this very basic understanding that has practical implications to their financial security. This is but one example, among many, of the importance of accessible tools to understand and analyze concepts.
A unified system to understand and back test such financial concepts is not currently available to the average investor. Generally, it would be desirable for such a system to have a set of components or features to:                comprehensively depict data in the financial system across economic activity, accounting treatment, and capital markets;        view, choose and manipulate objects and their attributes within the financial system across multiple dimensions (for example, time),        perform statistical, mathematical, and financial operations and modeling on such objects and attributes,        present or incorporate a priori model(s) or concept(s) relating the behavior of the objects and their attributes, and        simulate decisions and analyze their results.        
The rate at which the average investor is likely to adopt such a system is likely to be far greater if presented in the context of a unified easy-to-use system having a graphical language environment in which data visualization is a key component. Furthermore, even if the system is not used directly by the average investor, the advantages of such a system will be an order of magnitude greater than what is presently available if used as a tool for investment consultants to communicate complex concepts when giving investment advice.
In general, the availability of such systems is limited, and within the available set, there are a number of limitations including, but not limited to:                the need for knowledge and skills pertaining to financial, mathematical, statistical, and data manipulation concepts, not commonly possessed by, or within the grasp of, the average investor;        the need for advanced programming techniques for data acquisition, transformation, processing, and statistical modeling, not commonly possessed by the general public;        the need to perform exceedingly manual and error prone processes and methods using general purpose spreadsheet applications;        a reliance on a disparate combination of general and special purpose tools, each catering separately to one or more of the components or features described above, and not compatible with one another, or capable of seamlessly working together;        the lack of a capability to present or construct an a priori model within the system, forcing the user to keep track of the same and associated data manually outside the system; and        the lack of a comprehensive depiction of the financial system as described above, forcing the user to construct the connections between economic activity, accounting treatment, and capital markets.        
There are currently computer applications and programs that allow an investor to monitor and manage their investments using graphical user interfaces to communicate information to the investor. One such program provides the user with spreadsheets and graphs of investments that allow the investor to track each investment and determine how each is doing.
Other prior art programs provide complex three dimensional color coded graphs and graphics that communicate complex information to the user in a form that is compact and user friendly. For example, one such program provides a three-dimensional landscape in which values for multiple series of received data points are shown in arrays of discrete graphical elements. Each discrete graphical element has a visual attribute that represents one of the observed market values. The three-dimensional landscape can be updated in real time based on updated market values. Another prior art program takes historical data and modifies a functional representation in order to determine the result of a counterfactual test. However, these programs fails to provide the user with a means by which the user can interact to test out particular concepts to be simulated in order to learn by doing. Furthermore, such programs do not create a uniform and coherent environment in which the user can navigate to explore and modify the data to be used in experiments and simulations.
Still further, such programs lack the ability to teach the user how to improve their skills in understanding complex underlying concepts that would allow an investor to improve the types of investments in which the investor might select systematically at the right point in time. Spreadsheets and charts that provide the investor with information about particular assets that the investor has already purchased or which the investor is considering investing are of significant value, but do not assist the investor in gaining an understanding of the nature of the concepts that are required in order to make prudent choices and purchases. While some computer programs provide projections into the future, which can assist an investor in making choices, the investor never gains any understanding of the reasoning behind the projections or the concepts that lead the program to make the projections. Furthermore, such programs do not provide a uniform and coherent environment in which the user can navigate.
Accordingly, there is presently a need for systems and methods that allow a person with minimal knowledge and skill in finance, math, statistics and data manipulation to experiment with concepts, learn how concepts impact particular decisions and how such concepts can guide investment decisions, navigate through a uniform and coherent environment in which parameters can be selected and adjusted to gain the maximum value from the learning experience and which can run simulations of particular concepts to train and educate the user.